TimeGAN-pytorch
Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19.
Jinsung Yoon, Daniel Jarrett
Dependencies
- Python (>=3.7)
- Pytorch (>=1.7.0)
Pytorch implementation of the paper Time-series Generative Adversarial Networks
Overview
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Comments
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A bug here
opened by stillwang96 0Hi, when I was training, it came out that:
Encoder training step: 49998/50000 Loss: tensor(0.1051, device='cuda:0', grad_fn=
) Encoder training step: 49999/50000 Traceback (most recent call last): File "F:\codes\TimeGAN-pytorch-main\train.py", line 56, in train() File "F:\codes\TimeGAN-pytorch-main\train.py", line 53, in train model.train() File "F:\codes\TimeGAN-pytorch-main\lib\timegan.py", line 164, in train self.train_one_iter_s() File "F:\codes\TimeGAN-pytorch-main\lib\timegan.py", line 115, in train_one_iter_s self.optimize_params_s() File "F:\codes\TimeGAN-pytorch-main\lib\timegan.py", line 395, in optimize_params_s self.backward_s() File "F:\codes\TimeGAN-pytorch-main\lib\timegan.py", line 356, in backward_s self.err_s.backward(retain_graph=True) File "C:\Users\Admin\anaconda3\envs\gan\lib\site-packages\torch_tensor.py", line 396, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "C:\Users\Admin\anaconda3\envs\gan\lib\site-packages\torch\autograd_init_.py", line 173, in backward Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass RuntimeError: cudnn RNN backward can only be called in training mode Process finished with exit code 1
So how can I solve it?
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